Despite continuing improvements in automotive safety technology, automobile accidents remain a leading cause of death and serious injury. Recently, efforts have been made to apply advances in computing technology to improve automotive safety. One promising area has been the use of various sensors inside and outside of the vehicle to warn the driver of potentially hazardous conditions (e.g., lane departure warning systems) or to even to implement adjustments to the vehicle's operation to ensure safety (e.g., antilock brakes).
However, existing approaches use exclusively biometrics (e.g., artificial passengers) or exclusively vehicle sensors (e.g., “black box” devices). Furthermore, existing approaches teach only passively monitoring these sensors. Likewise, existing approaches teach only monitoring this data with regard to one vehicle at a time. Accordingly, it would be highly desirable to provide improved techniques in the integration of biometric sensors in automotive safety technology in order to provide enhanced detection and management of vehicular emergencies.